Datasets:
File size: 11,000 Bytes
23588d6 f04e8ac 23588d6 0e901ee e163557 b1e9ff6 6ca8029 cf26c82 2342327 0e901ee 23588d6 8dd9bcc 56b4304 a792bfc 028aa0a 140bb40 d9f82b9 5d59dff 23588d6 0e901ee e163557 b1e9ff6 6ca8029 cf26c82 2342327 23588d6 8dd9bcc 56b4304 a792bfc 028aa0a 140bb40 d9f82b9 5d59dff f04e8ac 6868835 95c6d27 6868835 6326969 6868835 72acec1 25b0559 6868835 7ce622a 6868835 ef5c383 6868835 d554682 6868835 ef5c383 6868835 d554682 ef5c383 6868835 95c6d27 6868835 49a4d9e 6868835 49a4d9e 6868835 95c6d27 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 |
---
language:
- afr
- amh
- arb
- arq
- ary
- eng
- es
- hau
- hin
- ind
- kin
- mar
- pan
- tel
dataset_info:
- config_name: afr
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: float64
splits:
- name: test
num_bytes: 65243
num_examples: 375
- name: dev
num_bytes: 66249
num_examples: 375
download_size: 95864
dataset_size: 131492
- config_name: amh
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: float64
splits:
- name: train
num_bytes: 209475
num_examples: 992
- name: test
num_bytes: 36637
num_examples: 171
- name: dev
num_bytes: 19498
num_examples: 95
download_size: 153682
dataset_size: 265610
- config_name: arb
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: float64
splits:
- name: test
num_bytes: 110473
num_examples: 595
- name: dev
num_bytes: 5846
num_examples: 32
download_size: 72348
dataset_size: 116319
- config_name: arq
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: float64
splits:
- name: train
num_bytes: 170025
num_examples: 1261
- name: test
num_bytes: 79323
num_examples: 583
- name: dev
num_bytes: 12181
num_examples: 97
download_size: 149472
dataset_size: 261529
- config_name: ary
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: float64
splits:
- name: train
num_bytes: 382561
num_examples: 924
- name: test
num_bytes: 175568
num_examples: 426
- name: dev
num_bytes: 27975
num_examples: 71
download_size: 274828
dataset_size: 586104
- config_name: eng
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: float64
splits:
- name: train
num_bytes: 844975
num_examples: 5500
- name: test
num_bytes: 374647
num_examples: 2600
- name: dev
num_bytes: 36697
num_examples: 250
download_size: 868674
dataset_size: 1256319
- config_name: esp
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: float64
splits:
- name: train
num_bytes: 316713
num_examples: 1562
- name: test
num_bytes: 123222
num_examples: 600
- name: dev
num_bytes: 28981
num_examples: 140
download_size: 323584
dataset_size: 468916
- config_name: hau
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: float64
splits:
- name: train
num_bytes: 403474
num_examples: 1736
- name: test
num_bytes: 142238
num_examples: 603
- name: dev
num_bytes: 49236
num_examples: 212
download_size: 328542
dataset_size: 594948
- config_name: hin
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: float64
splits:
- name: test
num_bytes: 377385
num_examples: 968
- name: dev
num_bytes: 113047
num_examples: 288
download_size: 217493
dataset_size: 490432
- config_name: ind
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: float64
splits:
- name: test
num_bytes: 68185
num_examples: 360
- name: dev
num_bytes: 26579
num_examples: 144
download_size: 68263
dataset_size: 94764
- config_name: kin
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: float64
splits:
- name: train
num_bytes: 234520
num_examples: 778
- name: test
num_bytes: 67211
num_examples: 222
- name: dev
num_bytes: 30758
num_examples: 102
download_size: 219256
dataset_size: 332489
- config_name: mar
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: float64
splits:
- name: train
num_bytes: 555224
num_examples: 1155
- name: test
num_bytes: 139343
num_examples: 298
- name: dev
num_bytes: 146496
num_examples: 293
download_size: 381039
dataset_size: 841063
- config_name: pan
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: float64
splits:
- name: test
num_bytes: 307401
num_examples: 634
- name: dev
num_bytes: 117984
num_examples: 242
download_size: 166402
dataset_size: 425385
- config_name: tel
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: float64
splits:
- name: train
num_bytes: 561688
num_examples: 1146
- name: test
num_bytes: 145249
num_examples: 297
- name: dev
num_bytes: 64775
num_examples: 130
download_size: 347275
dataset_size: 771712
configs:
- config_name: afr
data_files:
- split: test
path: afr/test-*
- split: dev
path: afr/dev-*
- config_name: amh
data_files:
- split: train
path: amh/train-*
- split: test
path: amh/test-*
- split: dev
path: amh/dev-*
- config_name: arb
data_files:
- split: test
path: arb/test-*
- split: dev
path: arb/dev-*
- config_name: arq
data_files:
- split: train
path: arq/train-*
- split: test
path: arq/test-*
- split: dev
path: arq/dev-*
- config_name: ary
data_files:
- split: train
path: ary/train-*
- split: test
path: ary/test-*
- split: dev
path: ary/dev-*
- config_name: eng
data_files:
- split: train
path: eng/train-*
- split: test
path: eng/test-*
- split: dev
path: eng/dev-*
- config_name: esp
data_files:
- split: train
path: esp/train-*
- split: test
path: esp/test-*
- split: dev
path: esp/dev-*
- config_name: hau
data_files:
- split: train
path: hau/train-*
- split: test
path: hau/test-*
- split: dev
path: hau/dev-*
- config_name: hin
data_files:
- split: test
path: hin/test-*
- split: dev
path: hin/dev-*
- config_name: ind
data_files:
- split: test
path: ind/test-*
- split: dev
path: ind/dev-*
- config_name: kin
data_files:
- split: train
path: kin/train-*
- split: test
path: kin/test-*
- split: dev
path: kin/dev-*
- config_name: mar
data_files:
- split: train
path: mar/train-*
- split: test
path: mar/test-*
- split: dev
path: mar/dev-*
- config_name: pan
data_files:
- split: test
path: pan/test-*
- split: dev
path: pan/dev-*
- config_name: tel
data_files:
- split: train
path: tel/train-*
- split: test
path: tel/test-*
- split: dev
path: tel/dev-*
task_categories:
- text-classification
- sentence-similarity
---
## Dataset Description
- **Homepage:** https://semantic-textual-relatedness.github.io
- **Repository:** [GitHub](https://github.com/semantic-textual-relatedness/Semantic_Relatedness_SemEval2024)
- **Paper:** [SemRel2024: A Collection of Semantic Textual Relatedness Datasets for 14 Languages](https://arxiv.org/abs/2402.08638)
- **Paper:** [SemEval Task 1: Semantic Textual Relatedness for African and Asian Languages](https://arxiv.org/pdf/2403.18933.pdf)
- **Leaderboard:** https://codalab.lisn.upsaclay.fr/competitions/16799#results
- **Point of Contact:** [Nedjma Ousidhoum](mailto:nedjma.ousidhoum@gmail.com)
### Dataset Summary
SemRel2024 is a collection of Semantic Textual Relatedness (STR) datasets for 14 languages, including African and Asian languages. The datasets are composed of sentence pairs, each assigned a relatedness score between 0 (completely) unrelated and 1 (maximally related) with a large range of expected relatedness values.
SemRel2024 dataset was used as part of the SemEval2024 shared task 1. The task aims to evaluate the ability of systems to measure the semantic relatedness between two sentences.
### Languages
The SemRel2024 dataset covers the following 14 languages:
1. Afrikaans (_afr_)
2. Algerian Arabic (_arq_)
3. Amharic (_amh_)
4. English (_eng_)
5. Hausa (_hau_)
6. Indonesian (_ind_)
7. Hindi (_hin_)
8. Kinyarwanda (_kin_)
9. Marathi (_mar_)
10. Modern Standard Arabic (_arb_)
11. Moroccan Arabic (_ary_)
12. Punjabi (_pan_)
13. Spanish (_esp_)
14. Telugu (_tel_)
**Note**: Spanish test labels are all -1 because the Spanish team retained the gold test labels to avoid contamination problems in future benchmarking. We refer to the [CodaLab contest website](https://codalab.lisn.upsaclay.fr/competitions/15715) to evaluate your predictions, which will remain open.
## Dataset Structure
### Data Instances
Each instance in the dataset consists of two text segments and a relatedness score indicating the degree of semantic relatedness between them.
```
{
"sentence1": "string",
"sentence2": "string",
"label": float
}
```
- sentence1: a string feature representing the first text segment.
- sentence2: a string feature representing the second text segment.
- label: a float value representing the semantic relatedness score between sentence1 and sentence2, typically ranging from 0 (not related at all) to 1 (highly related).
## Citation Information
If you use the SemRel2024 dataset in your research, please cite the following papers:
```
@misc{ousidhoum2024semrel2024,
title={SemRel2024: A Collection of Semantic Textual Relatedness Datasets for 14 Languages},
author={Nedjma Ousidhoum and Shamsuddeen Hassan Muhammad and Mohamed Abdalla and Idris Abdulmumin and Ibrahim Said Ahmad and
Sanchit Ahuja and Alham Fikri Aji and Vladimir Araujo and Abinew Ali Ayele and Pavan Baswani and Meriem Beloucif and
Chris Biemann and Sofia Bourhim and Christine De Kock and Genet Shanko Dekebo and
Oumaima Hourrane and Gopichand Kanumolu and Lokesh Madasu and Samuel Rutunda and Manish Shrivastava and
Thamar Solorio and Nirmal Surange and Hailegnaw Getaneh Tilaye and Krishnapriya Vishnubhotla and Genta Winata and
Seid Muhie Yimam and Saif M. Mohammad},
year={2024},
eprint={2402.08638},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```
@inproceedings{ousidhoum-etal-2024-semeval,
title = "{S}em{E}val-2024 Task 1: Semantic Textual Relatedness for African and Asian Languages",
author = "Ousidhoum, Nedjma and Muhammad, Shamsuddeen Hassan and Abdalla, Mohamed and Abdulmumin, Idris and
Ahmad,Ibrahim Said and Ahuja, Sanchit and Aji, Alham Fikri and Araujo, Vladimir and Beloucif, Meriem and
De Kock, Christine and Hourrane, Oumaima and Shrivastava, Manish and Solorio, Thamar and Surange, Nirmal and
Vishnubhotla, Krishnapriya and Yimam, Seid Muhie and Mohammad, Saif M.",
booktitle = "Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)",
year = "2024",
publisher = "Association for Computational Linguistics"
}
```
|